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In the field of connectomics, neuroscientists seek to identify corticalconnectivity comprehensively. Neuronal boundary detection from the ElectronMicroscopy (EM) images is often done to assist the automatic reconstruction ofneuronal circuit. But the segmentation of EM images is a challenging problem,as it requires the detector to be able to detect both filament-like thin andblob-like thick membrane, while suppressing the ambiguous intracellularstructure. In this paper, we propose multi-stage multi-recursive-input fullyconvolutional networks to address this problem. The multiple recursive inputsfor one stage, i.e., the multiple side outputs with different receptive fieldsizes learned from the lower stage, provide multi-scale contextual boundaryinformation for the consecutive learning. This design isbiologically-plausible, as it likes a human visual system to compare differentpossible segmentation solutions to address the ambiguous boundary issue. Ourmulti-stage networks are trained end-to-end. It achieves promising results ontwo public available EM segmentation datasets, the mouse piriform cortexdataset and the ISBI 2012 EM dataset.

Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection